Batch1 HILIC NEG untargeted metabolomics
Ontology highlight
ABSTRACT:
TISSUE(S): ['blood Serum']
SUBMITTER: Zheng-Jiang Zhu
PROVIDER: OEX00027836 | NODE | 2024-05-13
REPOSITORIES: NODE
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Project description:Batch2 HILIC NEG untargeted metabolomics
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Project description:Batch2 HILIC POS untargeted metabolomics
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Project description:An untargeted metabolomics strategy using hydrophilic interaction chromatography-mass spectrometry (HILIC-MS) was developed in this work enabling the study of the coffee roasting process. Green coffee beans and coffee beans submitted to three different roasting degrees (light, medium, and strong) were analyzed. Chromatographic separation was carried out using water containing 10 mM ammonium formate with 0.2 % formic acid (mobile phase A) and acetonitrile containing 10 mM ammonium formate with 0.2 % formic acid (mobile phase B). A total of 93 molecular features were considered from which 31 were chosen as the most statistically significant using variable in the projection values. 13 metabolites were tentatively identified as potential biomarkers of the coffee roasting process using this metabolomic platform. Results obtained in this work were complementary to those achieved using orthogonal techniques such as reversed-phase liquid chromatography-mass spectrometry (RPLC-MS) and capillary electrophoresis-mass spectrometry (CE-MS) since only one metabolite was found to be common between HILIC-MS and RPLC-MS platforms (caffeoylshikimic acid isomer) and other between HILIC-MS and CE-MS platforms (choline). On the basis of these results, an untargeted metabolomics multiplatform is proposed in this work based on the integration of the three orthogonal techniques as a powerful tool to expand the coverage of the roasted coffee metabolome.
| S-EPMC7070313 | biostudies-literature
Project description:Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time-m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine.
| S-EPMC10932214 | biostudies-literature
Project description:IntroductionRecent advances in high-throughput methodologies in the 'omics' and synthetic biology fields call for rapid and sensitive workflows in the metabolic phenotyping of complex biological samples.ObjectiveThe objective of this research was to evaluate a straightforward to implement LC-MS metabolomics method using a commercially available chromatography column that provides increased throughput. Reducing run time can potentially impact chromatography and therefore the effects of ion mobility spectrometry to expand peak capacity were also evaluated. Additional confidence provided via collision cross section measurements for detected features was also explored.MethodsA rapid untargeted metabolomics workflow was developed with broad metabolome coverage, combining zwitterionic-phase hydrophilic interaction chromatography (HILIC-Z) with drift tube ion mobility-quadrupole time-of-flight (DTIM-qTOF) mass spectrometry. The analytical performance of our method was explored using extracts from complex biological samples, including a reproducibility study on chicken serum and a simple comparative study on a bacterial metabolome.ResultsThe method is acronymised RHIMMS for rapid HILIC-Z ion mobility mass spectrometry. We present the RHIMMS workflow starting with data acquisition, followed by data processing and analysis. RHIMMS demonstrates improved chromatographic separation for a selection of metabolites with wide physicochemical properties while maintaining reproducibility at better than 20% over 200 injections at 3.5 min per sample for the selected metabolites, and a mean of 13.9% for the top 50 metabolites by intensity. Additionally, the combination of rapid chromatographic separation with ion mobility allows improved annotation and the ability to distinguish isobaric compounds.ConclusionOur results demonstrate RHIMMS to be a rapid, reproducible, sensitive and high-resolution analytical platform that is highly applicable to the untargeted metabolomics analysis of complex samples.
| S-EPMC8885480 | biostudies-literature
Project description:Perennial ryegrass (<i>Lolium perenne</i>) is integral to temperate pastoral agriculture, which contributes most of the milk and meat production worldwide. Chemical profiles and diversity of ryegrass offer several opportunities to harness specific traits and elucidate underlying biological mechanisms for forage improvement. We conducted a large-scale metabolomics study of perennial ryegrass comprising 715 genotypes, representing 118 populations from 21 countries. Liquid/gas chromatography-mass spectrometry based targeted and non-targeted techniques were used to analyse fructan oligosaccharides, lipids, fatty acid methyl esters, polar and semi-polar compounds. Fructan diversity across all genotypes was evaluated, high- and low-sugar groups identified, and fructan accumulation mechanisms explored. Metabolites differentiating the two groups were characterised, modules and pathways they represent deduced, and finally, visualisation and interpretation provided in a biological context. We also demonstrate a workflow for large-scale metabolomics studies from raw data through to statistical and pathway analysis. <br><br> <b>UPLC-MS HILIC NEG assay</b> is reported in the current study MTBLS63.<br> <b>UPLC-MS HILIC POS assay</b> is reported in <a href=https://www.ebi.ac.uk/metabolights/MTBLS62>MTBLS62</a>.<br> <b>UPLC-MS C18 reverse phase POS assay</b> is reported in <a href=https://www.ebi.ac.uk/metabolights/MTBLS64>MTBLS64</a>.<br> <b>UPLC-MS C18 reverse phase NEG assay</b> is reported in <a href=https://www.ebi.ac.uk/metabolights/MTBLS65>MTBLS65</a>.<br> <b>UPLC-MS C1 reverse phase POS and GC-MS FAME assays</b> are reported in <a href="https://www.ebi.ac.uk/metabolights/MTBLS66">MTBLS66</a>.<br> <b>UPLC-MS C1 reverse phase NEG assay</b> is reported in <a href=https://www.ebi.ac.uk/metabolights/MTBLS68>MTBLS68</a>.<br> <b>HPLC-MS Hypercarb reverse phase NEG assay</b> is reported in <a href=https://www.ebi.ac.uk/metabolights/MTBLS529>MTBLS529</a>.<br>
2019-03-01 | MTBLS63 | MetaboLights